Scholar
Jianhong Wang
Google Scholar ID: K1FKF3IAAAAJ
University of Bristol
Citations & Impact
Citations
943
H-index
14
i10-index
16
Publications
20
Co-authors
11
list available
Publications
Resume (English only)
Academic Achievements
- Organized multiple workshops on Multi-Agent Reinforcement Learning, including 'The 2nd Coordination and Cooperation in Multi-Agent Reinforcement Learning' at Reinforcement Learning Conference 2025, 'Multi-Agent Reinforcement Learning Workshop' at Distributed Artificial Intelligence 2024 (DAI 2024), and 'Coordination and Cooperation in Multi-Agent Reinforcement Learning' at Reinforcement Learning Conference 2024 (RLC 2024). These workshops aim to advance cooperative and coordinated multi-agent reinforcement learning in both theory and real-world applications.
Research Experience
- Senior Research Associate at INFORMED-AI Hub, working with Prof Jonathan Lawry at University of Bristol, and a member of ELLIS. Previously, a Postdoctoral Research Associate at University of Manchester, UK, working with Prof Samuel Kaski.
Education
- Ph.D. in Electrical and Electronic Engineering Research from Imperial College London, UK, in 2024, supervised by Dr Yunjie Gu, Prof Tim C. Green, and Prof Tae-Kyun Kim. M.Res. in Web Science and Big Data Analytics from University College London (UCL), UK, with distinction, in 2018, supervised by Prof Jun Wang. M.Sc. in Computing (Machine Learning) from Imperial College London, UK, with merit, in 2017, supervised by Prof Björn Schuller for the master project. B.Eng. in Computer Science and Electronic Engineering from University of Liverpool, UK, with first-class honours, in 2016, supervised by Prof Danushka Bollegala and Prof Karl Tuyls for the final year project.
Background
- Primary research interests: Multi-Agent Reinforcement Learning, Robust Reinforcement Learning, and Ad Hoc Teamwork. Focus on designing algorithms through the lens of Cooperative Game Theory. Aims to integrate fundamental structures from Game Theory into the development of learning algorithms to enhance interpretability, transparency, and reliability for contemporary learning-based Multi-Agent Systems. Passionate about practical applications of Machine Learning (especially Multi-Agent Reinforcement Learning) in Autonomous Systems like Smart Grids, Robotics, and Dialogue Systems.
